The embodiment disclosed herein has a focus on combined visible and near-infrared (NIR) spectroscopy and its modes of use, major issues in the application of NIR to the measurement of O—H, N—H and C—H containing molecules that are indicators of sample quality including fruit quality and in particular tree fruit quality.
Near-Infrared Spectroscopy Background; Near-infrared spectroscopy has been used since the 1970's for the compositional analysis of low moisture food products. However, only in the last 10-15 years has NIR been successfully applied to the analysis of high moisture products such as fruit. NIR is a form of vibrational spectroscopy that is particularly sensitive to the presence of molecules containing C—H (carbon-hydrogen), O—H (oxygen-hydrogen), and N—H (nitrogen-hydrogen) groups. Therefore, constituents such as sugars and starch (C—H), moisture, alcohols and acids (O—H), and protein (N—H) can be quantified in liquids, solids and slurries. In addition, the analysis of gases (e.g., water vapor, ammonia) is possible. NIR is not a trace analysis technique and it is generally used for measuring components that are present at concentrations greater than 0.1%.
Short-Wavelength NIR vs. Long-Wavelength NIR; NIR has traditionally been carried out in the 1100-2500 nm region of the electromagnetic spectrum. However, the wavelength region of ˜700-1100 nm (short wavelength-NIR or SW-NIR) has been gaining increased attention. The SW-NIR region offers numerous advantages for on-line and in-situ bulk constituent analysis. This portion of the NIR is accessible to low-cost, high performance silicon detectors and fiber optics. In addition, high intensity laser diodes and low-cost light emitting diodes are becoming increasingly available at a variety of NIR wavelength outputs.
The relatively low extinction (light absorption) coefficients in the SW-NIR region yields linear absorbance with analyte concentration and permits long, convenient pathlengths to be used. The depth of penetration of SW-NIR is also much greater than that of the longer wavelength NIR, permitting a more adequate sampling of the “bulk” material. This is of particular importance when the sample to be analyzed is heterogeneous such as fruit.
Diffuse Reflectance Sampling vs. Transmission Sampling:Traditional NIR analysis has used diffuse reflectance sampling. This mode of sampling is convenient for samples that are highly light scattering or samples for which there is no physical ability to employ transmission spectroscopy. Diffusely reflected light is light that has entered a sample, undergone multiple scattering events, and emerged from the surface in random directions. A portion of light that enter the sample is also absorbed. The depth of penetration of the light is highly dependent on the sample characteristics and is often affected by the size of particles in the sample and the sample density. Furthermore, diffuse reflectance is biased to the surface of a sample and may not provide representative data for large heterogeneous samples such as apples.
While transmission sampling is typically used for the analysis of clear solutions, it also can be used for interrogating solid samples. A transmission measurement is usually performed with the detector directly opposite the light source (i.e., at 180 degrees) and with the sample in the center. Alternately the detector can be placed closer to the light source (at angles less than 180 degrees), which is often necessary to provide a more easily detected level of light. Because of the long sample pathlengths and highly light scattering nature of most tree fruit, transmission measurements can only be performed in the SW-NIR wavelength region, unless special procedures are employed to improve signal to noise.
NIR Calibration: NIR analysis is largely an empirical method; the spectral lines are difficult to assign, and the spectroscopy is frequently carried out on highly light scattering samples where adherence to Beer's Law is not expected. Accordingly, statistical calibration techniques are often used to determine if there is a relationship between analyte concentration (or sample property) and instrument response. To uncover this relationship requires a representative set of “training” or calibration samples. These samples must span the complete range of chemical and physical properties of all future samples to be seen by the instrument.
Calibration begins by acquiring a spectrum of each of the samples. Constituent values for all of the analytes of interest are then obtained using the best reference method available with regards to accuracy and precision. It is important to note that a quantitative spectral method developed using statistical correlation techniques can perform no better than the reference method.
After the data has been acquired, computer models employing statistical calibration techniques are developed that relate the NIR spectra to the measured constituent values or properties. These calibration models can be expanded and must be periodically updated and verified using conventional testing procedures.
Factors affecting calibration include fruit type and variety, seasonal and geographical differences, and whether the fruit is fresh or has been in cold or other storage. Calibration variables include the particular properties or analytes to be measured and the concentration or level of the properties. Intercorrelations (co-linearity) should be minimized in calibration samples so as not to lead to false interpretation of a models predictive ability. Co-linearity occurs when the concentrations of two components are correlated, e.g., an inverse correlation exists when one component is high, the other is always low or vice versa.
Application of NIR to Tree Fruit and Existing On-Line NIR Instrumentation: A growing body of research exists for NIR analysis of tree fruit. NIR has been used for the measurement of fruit juice, flesh, and whole fruit. In juice, the individual sugars (sucrose, fructose, glucose) and total acidity can be quantified with high correlation (>0.95) and acceptable error. Individual sugars can not be readily measured in whole fruit. Brix is the most successfully measured NIR parameter in whole fruit and can generally be achieved with an error of ±0.5-1.0 Brix. More tentative recent research results indicate firmness and acidity measurement in whole fruit also may be possible.
Only in Japan has the large-scale deployment of on-line NIR for fruit sorting occurred. These instruments require manual placement/orientation of the fruit prior to measurement and early versions were limited to a measurement rate of three samples per second. The Japanese NIR instruments are also limited to a single lane of fruit and appear to be difficult to adapt to multi-lane sorting equipment used in the United States of America. While earlier Japanese NIR instruments employed reflectance sampling, more recent instruments use transmission sampling.
In Koashi et al., U.S. Pat. No. 4,883,953, there is described a method and apparatus for measuring sugar concentrations in liquids. Measurements are made at two different depths using weak and strong infrared radiation. The level of sugar at depths between these two depths can then be measured. The method and apparatus utilizes wavelength bands of 950-1,150 nm, 1,150-1,300 nm, and 1,300-1,450 nm.
U.S. Pat. No. 5,089,701, to Dull et al., uses near infrared (NIR) radiation in the wavelength range of 800-1,050 nm to demonstrate measurement of soluble solids in Honeydew melons. An eight-centimeter or greater distance between the light delivery location to the fruit and the light collection location was found to be necessary to accurately predict soluble solids because of the thick rind.
Iwamoto et al., U.S. Pat. No. 5,324,945, also use NIR radiation to predict sugar content of mandarin oranges. Iwamoto utilizes a transmission measurement arrangement whereby the light traverses through the entire sample of fruit and is detected at 180 degrees relative to the light input angle. Moderately thick-skinned fruit (mandarin oranges) were used to demonstrate the method, which relies on a fruit diameter correction by normalizing (dividing) the spectra at 844 nm, where, according to the disclosed data, correlation with the sugar content is lowest. NIR wavelengths in the range of 914-919 nm were found to have the highest correlation with sugar content. Second, third and fourth wavelengths that were added to the multiple regression analysis equation used to correlate the NIR spectra with sugar content were 769-770 nm, 745 nm, and 785-786 nm.
In U.S. Pat. No. 5,708,271, Ito et al. demonstrates a sugar content measuring apparatus that utilizes three different NIR wavelengths in the range from 860-960 nm. The angle between light delivery and collection was varied between 0 and 180 degrees and it was concluded that the low NIR radiation levels that must be detected when a photo-detector is placed at 180 degrees relative to the radiation source are not desirable because of the more complicated procedures and equipment that are required. A correlation of NIR absorbance with sugar content of muskmelons and watermelons was found when an intermediate angle, which gave greater NIR radiation intensity, was detected. No size correction was necessary with this approach.
U.S. Pat. No. 4,883,953 to Koashi et al. uses comparatively long wavelengths of NIR radiation (i.e., >950 nm), while in U.S. Pat. Nos. 5,089,701 to Dull, and 5,708,271 to Ito, wavelengths of NIR radiation used are greater than 800 nm and 860 nm, respectively. In U.S. Pat. No. 5,324,945 to Iwamoto, the wavelengths of NIR radiation with the highest correlation to sugar content of mandarins were 914 nm or 919 nm, when the fruit were measured on the equatorial or stem portion, respectively. All of these methods use near-infrared wavelengths of light to correlate with sugar content of whole fruit. No other quality parameters are measured by these techniques.
The four disclosed patents are similar to the apparatus and method described here in that the present disclosure also measures sugar content. Two of the patents (U.S. Pat. Nos. 5,089,701 and 5,324,945) NIR wavelengths less than 850 nm) U.S. Pat. No. 5,089,701 discloses the operation of the invention within the range of “from about 800 nanometers to about 1050 nanometers.” U.S. Pat. No. 5,324,945 lists 914 nm or 919 nm as the primary analytical wavelength correlated with whole fruit sugar content; multiple linear regression was used to add successive wavelengths to the model as follows: 769-770 nm (2nd wavelength added), 745 nm (3rd wavelength added), and 785-786 nm (4th wavelength added). In U.S. Pat. No. 5,089,701, addition of the fourth wavelength to the model only reduced the standard error of prediction (SEP) by 0.1-0.2 Brix, which is approaching or less than the error limits of the refractometer used to determine the reference (“true”) Brix values.
Other similarities between the method and apparatus described herein with the four patents listed above include the use of multivariate statistical analysis to establish correlation of the near-infrared spectral data with sugar content of whole fruit. Most also use data processing techniques such as second derivative transformation and some type of spectral normalization. All of these methods for relating NIR spectra to chemical or physical properties are well known to those practiced in the art of NIR spectroscopy.